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[HTML][HTML] ERS/ATS technical standard on interpretive strategies for routine lung function tests
S Stanojevic, DA Kaminsky, MR Miller… - European …, 2022 - publications.ersnet.org
Background Appropriate interpretation of pulmonary function tests (PFTs) involves the
classification of observed values as within/outside the normal range based on a reference …
classification of observed values as within/outside the normal range based on a reference …
Treatment trials in young patients with chronic obstructive pulmonary disease and pre–chronic obstructive pulmonary disease patients: time to move forward
FJ Martinez, A Agusti, BR Celli, MLK Han… - American journal of …, 2022 - atsjournals.org
Chronic obstructive pulmonary disease (COPD) is the end result of a series of dynamic and
cumulative gene–environment interactions over a lifetime. The evolving understanding of …
cumulative gene–environment interactions over a lifetime. The evolving understanding of …
Artificial intelligence and machine learning in chronic airway diseases: focus on asthma and chronic obstructive pulmonary disease
Y Feng, Y Wang, C Zeng, H Mao - International journal of …, 2021 - pmc.ncbi.nlm.nih.gov
Chronic airway diseases are characterized by airway inflammation, obstruction, and
remodeling and show high prevalence, especially in develo** countries. Among them …
remodeling and show high prevalence, especially in develo** countries. Among them …
Deep learning–based approach to predict pulmonary function at chest CT
Background Low-dose chest CT screening is recommended for smokers with the potential
for lung function abnormality, but its role in predicting lung function remains unclear …
for lung function abnormality, but its role in predicting lung function remains unclear …
Fx-Net and PureNet: Convolutional Neural Network architecture for discrimination of Chronic Obstructive Pulmonary Disease from smokers and healthy subjects …
C Avian, MI Mahali, NAS Putro, SW Prakosa… - Computers in Biology …, 2022 - Elsevier
As one of the most reliable and significant indicators, Chronic Obstructive Pulmonary
Disease (COPD) becomes a robust predictor of lung cancer early detection, the world's …
Disease (COPD) becomes a robust predictor of lung cancer early detection, the world's …
Deep learning parametric response map** from inspiratory chest CT scans: a new approach for small airway disease screening
Objectives Parametric response map** (PRM) enables the evaluation of small airway
disease (SAD) at the voxel level, but requires both inspiratory and expiratory chest CT …
disease (SAD) at the voxel level, but requires both inspiratory and expiratory chest CT …
Analyzing the use of artificial intelligence for the management of chronic obstructive pulmonary disease (COPD)
ADR Fernández, DR Fernández, VG Iglesias… - International journal of …, 2022 - Elsevier
Objective Chronic obstructive pulmonary disease (COPD) is a disease that causes airflow
limitation to the lungs and has a high morbidity around the world. The objective of this study …
limitation to the lungs and has a high morbidity around the world. The objective of this study …
Application of machine learning in pulmonary function assessment where are we now and where are we going?
Analysis of pulmonary function tests (PFTs) is an area where machine learning (ML) may
benefit clinicians, researchers, and the patients. PFT measures spirometry, lung volumes …
benefit clinicians, researchers, and the patients. PFT measures spirometry, lung volumes …
SmoothHess: ReLU network feature interactions via stein's lemma
Several recent methods for interpretability model feature interactions by looking at the
Hessian of a neural network. This poses a challenge for ReLU networks, which are …
Hessian of a neural network. This poses a challenge for ReLU networks, which are …
Aritificial Inteligence Challenges in COPD management: a review
Machine learning algorithms have been drawing attention in lung disease research.
However, due to their algorithmic learning complexity and the variability of their architecture …
However, due to their algorithmic learning complexity and the variability of their architecture …